What is an AI Readiness Report?

An AI Readiness Report is a comprehensive audit that evaluates how prepared a codebase is for autonomous AI agents by analyzing documentation, testing infrastructure, and build systems.

Does this skill provide automated fixes?

Yes, after identifying gaps, the skill can automate common remediation tasks such as generating AGENTS.md files, setting up pre-commit hooks, or adding issue templates.

How do I run a readiness report in Claude Code?

You can trigger the analysis by using the /readiness-report command, which will scan your repository structure and configuration files.

Why is documentation a pillar for AI readiness?

Clear documentation like AGENTS.md provides autonomous agents with the necessary context on architecture and workflows, preventing them from wasting cycles on preventable errors.

What are the five maturity levels?

Levels range from L1 (Initial), where only manual assistance is possible, to L5 (Optimized), where the codebase supports full autonomous end-to-end development.

AI Readiness Report

Name: AI Readiness Report
Author: OpenHands

byOpenHands

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Deployment & DevOps

Evaluates repositories across eight technical pillars to determine how well a codebase supports autonomous AI-assisted development.

About

The Readiness Report skill helps developers transform their repositories into AI-friendly environments by identifying technical gaps that cause autonomous agents to fail. By analyzing 81 specific criteria—including build systems, testing coverage, and documentation—the skill assigns a maturity level from L1 to L5. This allows teams to pinpoint exactly why an AI might struggle with their code and provides a clear roadmap for remediation, including automated fixes for common issues like missing AGENTS.md files or PR templates.

Key Features

Identification of AI-blocking gaps in CI/CD, testing, and documentation
Prioritized remediation list to improve agentic development efficiency
Automated generation of essential AI context files like AGENTS.md
Maturity level scoring (L1-L5) with clear progression requirements
Comprehensive 81-point codebase analysis across 9 technical pillars
46 GitHub stars

Use Cases

Auditing a legacy repository before introducing AI coding assistants
Benchmarking engineering standards and CI/CD maturity for remote teams
Improving the success rate of autonomous agents on complex dev tasks

About

Key Features

Identification of AI-blocking gaps in CI/CD, testing, and documentation
Prioritized remediation list to improve agentic development efficiency
Automated generation of essential AI context files like AGENTS.md
Maturity level scoring (L1-L5) with clear progression requirements
Comprehensive 81-point codebase analysis across 9 technical pillars
46 GitHub stars

Use Cases

Auditing a legacy repository before introducing AI coding assistants
Benchmarking engineering standards and CI/CD maturity for remote teams
Improving the success rate of autonomous agents on complex dev tasks